具有可重入作业的双目标流水车间调度问题的混合遗传算法

2010 IEEE International Conference on Industrial Engineering and Engineering Management Pub Date : 2010-12-23 DOI:10.1109/IEEM.2010.5674366

C. K. Lee, D. Lin

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引用次数: 4

摘要

提出了具有可重入作业的流水车间问题的模拟遗传算法调度模型。本研究的目标是最小化加权延迟和完工时间。提出的模型认为，具有不同到期日的作业在机器上以相同的顺序进行处理。此外，重新进入的作业是随机的，因为只有一些作业需要重新进入流程车间。一旦工件重新进入车间，就会调整延迟权重。通过案例公司的数值实验，验证了该遗传算法的有效性。结果表明，该方法具有较高的订单满意率。

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Hybrid genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

This paper presents a simulated genetic algorithm model of scheduling the flow shop problems with re-entrant jobs. The objectives of this research are to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines with the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs re-enter the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the industrial practices.

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2010 IEEE International Conference on Industrial Engineering and Engineering Management

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